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Abstract:

An algorithm is proposed to eliminate from MRI images pixels which have
been incorrectly identified as corresponding to infarct material. A first
technique is to eliminate identified regions which are determined to be
similar to the region of the scan which corresponds to the identified
region reflected in the mid-sagittal plane (MSP) of the brain. A second
technique is to eliminate regions which are determined not to have
corresponding identified regions in one or more of the other scans. The
combination of two techniques enhances the confidence in the decision of
whether a hyperintense region is an infarct or artifact.

Claims:

1. A method of processing an MRI image of a brain, the MRI image
comprising a plurality of 2D MRI scans corresponding to respective planes
of the brain, the method including identifying in each scan one or more
hyperintense regions which are candidates to correspond to infarct tissue
in the brain;the method further comprising one or both of:(a) eliminating
identified regions in a brain hemisphere identified as containing an
infarct which are determined to meet a first similarity criterion with
respect to a corresponding region of the same scan located in a reflected
position about a mid-sagittal plane (MSP) of the scan; and(b) eliminating
identified regions which are determined not to correspond to any said
identified region in the corresponding location of the other said scans.

2. A method according to claim 1 which includes step (a), and in which
said similarity criterion is that the identified region and the
corresponding region have respective intensities which differ by less
than a value which is an increasing function of the intensity of the
identified region.

3. A method according to claim 2 in which the value is proportional to the
intensity of the identified region.

4. A method according to claim 1 which includes step (a) and further
includes:determining whether the region occupies more than a
predetermined proportion of the scan;if the determination is positive,
determining pixel-by-pixel in the identified region whether said
similarity criterion is met; andif the determination is negative,
determining whether the similarity criterion is met for the identified
region as a whole.

5. A method according to claim 1 including both steps (a) and (b) in that
order.

6. A method according to claim 1 and including step (b) in which any
identified regions are excluded which are determined not to correspond to
a said identified region in the corresponding location of the neighboring
scan.

7. A method according to claim 1 and including step (b), including a step
of determining, for each identified region of each scan, the number of
consecutive scans ν which contain an identified region in
corresponding locations, and determining among the determined values ν
the maximal value νmax, and terminating step (b) if νmax
is below a threshold.

8. A method according to claim 1 and including step (b), step (b) further
including dilating the identified regions using a structuring element.

9. A method according to claim 1 in which said step of identifying in each
scan one or more hyperintense regions which are candidates to correspond
to infarct tissue includes:(i) excluding regions of the scan which have
intensities below a first threshold;(ii) determining the mean intensity
of the remaining regions of the scan, and a measure of the error in the
mean intensity; and(iii) excluding regions of the scan have an intensity
which is below a function of the mean and the error.

10. A method of screening a set of 2D MRI scans corresponding to a
respective plurality of planes of a brain, the method
comprising:processing the scans by a method according to any of the
preceding claims and including step (a),determining for each scan whether
the area of the remaining identified regions is a above a threshold;
andif the determination is negative excluding the scan from the set of
scans.

11. A computer system arranged to perform a method according to claim 1.

[0002]Generally, there are two types of errors [1] in any observation:
systematic and random. Systematic errors tend to shift all measurements
in a particular direction. Some of the main reasons of such errors are
incorrect calibration of an instrument, improper use of the instrument,
etc. Large systematic errors can be often be eliminated (e.g. by applying
zero correction of the instrument or repeating the experiment), but small
systematic errors will always be present since no instrument can ever be
calibrated perfectly. This is the reason why several independent
confirmations of experimental results should be performed, preferably
using different techniques.

[0003]If an experiment is performed several times with all experimental
conditions constant, the outcome is still different. These fluctuations
in the outcome are called random errors (or statistical errors). The
value of the outcome is taken as the mean of observations and the
standard deviation is taken as the error on the mean. The standard
deviation can sometimes be obtained by repeating the experiment, but in
some practical situations it is impossible to repeat experiments. In
these situations, the knowledge of the distribution of outcome is applied
to predict statistical errors. The outcome usually follows certain known
distributions depending on the nature of the experiment e.g. the Poisson
distribution is a common outcome in experiments which include a count.
For the Poisson distribution, standard deviation (σ) is related to
mean (μ) as σ= {square root over (μ)} [1]. Because of the
relationship between μ and σ, one is able to predict an error
from the outcome of the experiment (where the outcome is a result of
counts per unit time); for example, Ref. [2] used a Poisson
distribution-based noise removal technique for nuclear medical imaging
since such imaging involves a number of decays per unit time.

[0004]The process of MRI acquisition is very complicated
(http://www.easymeasure.co.uk/principlesmri.aspx,
http://www.sunnybrook.ca/research/groups/cardiac_mri/MR_background,
accessed Oct. 23, 2007). MRI signal intensity has a complicated
dependence on many parameters including the count of magnetically excited
protons in the voxel during the image acquisition time. Since intensity
is in part also related to the count of magnetically excited protons, the
Poisson distribution is used to predict the distribution of intensity of
each pixel. In the light of this assumption, an error on the pixel
intensity can be predicted. Thus, the reported pixel value can be assumed
to have error equal to {square root over (p.sub.μ)} where p.sub.μ
is the mean pixel intensity of some hypothetical observations.

[0005]There are various kinds of acquisition artifacts associated with MRI
scans (some are describe at
http://www.mritutor.org/mritutor/artifact.htm, accessed Oct. 23, 2007)
such as motion artifacts, aliasing artifacts, susceptibility artifacts
etc. Some known methods to remove artifacts and reduce noise are as
follows. Ref [3] presents a Wavelet-based Rician noise removal for MRI.
Ref [4] describes an approach to noise filtering in multi-dimensional
data using a partial volume data density model. Ref [5] suggests
correcting bulk in-plane motion artifacts in MRI using a point spread
function. A more elaborate list of these methods is included in
(http://iris.usc.eduNision-Notes/bibliography/medical891.html, accessed
Oct. 23, 2007).

[0006]Computer aided detection (CAD) plays a significant role in aiding
accurate medical image interpretations in different areas [e.g. 6-10].
The present inventors have developed a suite of CAD systems for acute
ischemic and hemorrhagic strokes [11-13]. One of key algorithms is
segmentation of infarcts. Its accuracy depends on correct discrimination
of infarct from artifacts. Accurate and rapid quantification of infarcts
from DWI scans is critical in acute ischemic strokes. Acquisition
artifacts lead to hyperintense regions in DWI MR scans resulting in false
positives. Discriminating infarcts and artifacts helps to reduce infarct
segmentation errors.

[0008]In general terms, the algorithm proposes that an MRI image of a
brain, such as a 3D DWI image comprising a plurality of 2D DWI scans,
which has been segmented based on the intensity of the pixels in the scan
to identify hyperintense regions of a brain which are candidates to
correspond to infarct tissue, is processed to eliminate identified
regions for which this identification was incorrect. This is done by one
or more of: eliminating identified regions which are determined to be
similar to the region of the scan which corresponds to the identified
region reflected in the mid-sagittal plane (MSP) of the brain; and
eliminating regions which are determined not to have corresponding
identified regions in one or more of the other scans.

[0009]The proposed algorithm may make it possible to discriminate between
infarcts and artifacts in DWI scans, and thereby reduce errors in
morphological measurements.

[0010]The criterion for evaluating the similarity of symmetrically-related
hyperintense regions may employ a numerical parameter which is related to
the Poisson error in the intensity of each pixel. This is because the
expected error in the intensity of each pixel relative to a perfect
measurement of the intensity (the "intensity space" of the pixel) is
typically given by a normal distribution independently of the nature of
the experiment.

[0011]Two applications of the present technique are: determination that
there is insufficient evidence that a given 2D scan exhibits an infarct
(e.g. if, following one or both of the elimination processes proposed
above, and in particular the step of eliminating symmetric regions, the
amount of the remaining infarct regions does not meet a threshold); and,
in an 2D scan which does exhibit an infarct, removing regions which are
erroneously identified as infarcts.

[0012]The algorithm has the potential to remove artifacts from any infarct
processing system. In particular, this approach has application to
investigations of thrombolysis using DWT scans, and to quantify
morphological properties of a newly discovered infarct. Once the
algorithm above has been used to produce a post-processed image, that
image may be used to quantify (i) the diffusion perfusion mismatch and
(ii) size of infarct to that of MCA ratio.

[0013]Note that apart from DWI, other data acquisition techniques such as
FLAIR (fluid attenuation inversion recovery), T2, ADC (apparent diffusion
coefficient) are used for infarct staging. It is presently considered
that the present techniques are of most interest in quantifying
newly-identified infarcts, and for these DWI is most valuable, but the
technique is applicable wherever the "signal of interest" or "detection
of a disease" is brighter than the rest of the image. So irrespective of
the type of images this technique is applicable is applicable.

[0014]The present algorithm may be implemented by a computer system. If
so, it is typically performed automatically (which is here used to mean
that, although human interaction may initiate the algorithm, human
interaction is not required while the algorithm is carried out). The
algorithm might alternatively be performed semi-automatically (in which
case there is human interaction with the computer during the processing).

[0015]A specific expression of the invention is a method of processing an
MRI image of a brain, the MRI image comprising a plurality of 2D MRI
scans corresponding to respective planes of the brain, the method
including identifying in each scan one or more hyperintense regions which
are candidates to correspond to infarct tissue in the brain; [0016]the
method further comprising one or both of: [0017](a) eliminating
identified regions in a brain hemisphere identified as containing an
infarct which are determined to meet a first similarity criterion with
respect to a corresponding region of the same scan located in a reflected
position about a mid-sagittal plane (MSP) of the scan; and [0018](b)
eliminating identified regions which are determined not to correspond to
any said identified region in the corresponding location of the other
said scans.

BRIEF DESCRIPTION OF THE DRAWINGS

[0019]Embodiments of the invention will now be described, for the sake of
example only, with reference to the following drawings, in which:

[0020]FIG. 1 is a flowchart illustrating steps of first process which is
an embodiment of the invention, for eliminating pixels and regions which
are symmetrically related about the MSP;

[0021]FIG. 2 is composed of FIG. 2(a) and (b) which respectively
illustrate (a) similar pixels and (b) similar multi-pixel regions in
infarct (I) and non-infarct (N) hemispheres;

[0022]FIG. 3 is a histogram of pixel intensities a typical DWI MRI image
of a brain;

[0023]FIG. 4 is a flowchart illustrating sub-steps of first process which
is an embodiment of the invention, for eliminating regions which do not
have 3D spatial correlation;

[0024]FIG. 5 illustrates a structuring element used in the method of FIG.
4;

[0037]Two processes which are embodiments of the invention will now be
described. After this, we will discuss two applications which employ one
or both of the processes as part of more complex algorithms, which are
also embodiments of the invention. Finally we discuss experimental
results of the these two applications.

1.1 First Process: Elimination of Symmetric Artifacts

[0038]The input to the first process is a 2D DWI scan (or a plurality of
such slices, such as a plurality of axial scans of slices at respective
heights in a patient's brain).

[0039]The flowchart of the first process (symmetric artifact removal) is
presented in FIG. 1. This shows how the first process is used on a single
2D scan, but the process is typically performed separately for each of a
plurality of such scans. The symmetric regions in question are regions of
the same shape and size at the same perpendicular distance from MSP. This
is illustrated in FIG. 2(a) and (b), which illustrate respectively how
single pixels and multi-pixel regions may be symmetrically distributed
about the MSP, in infarct (I) and non-infarct (N) hemispheres.

[0040]The input 2D DWI scan is labeled in FIG. 1 as 1. In a first step 2
of the first process, the MSP of the 2D DWI image 1 is identified, e.g.
using a method disclosed by Nowinski et al (2006) [17]. The MSP divides
the image into two hemispheres, each side being a close approximation to
the mirror image of the other. Then the hemisphere which contains the
infarct is identified, e.g. using a method disclosed by Gupta et al
(2008) [14].

[0041]In a second step 3, the hyperintense regions in the infarct
hemisphere are labeled. This can be done by obtaining an intensity
histogram of the infarct hemisphere. As is known from the prior art, a
typical intensity histogram of an MRI image containing infarct material
is as shown in FIG. 1, and includes two peaks. The peak at higher
intensity is defined as T1, and is the approximate boundary between the
hyperintense and isointense normal tissue regions. Pixels which have
intensities equal to or greater than T1 are identified as hyperintense.
That is, the image is segmented, with each pixel being labeled as
hyperintense or not. These pixels may be isolated (i.e. single pixel
regions), or may be part of multi-pixel regions. In either case, the
regions are labeled as hyperintense regions. The regions are generated by
applying a segmentation algorithm such as [15]. The size of the region
are calculated using the total number of pixels in the segmented regions.

[0042]In step 4, for each hyperintense region in the infarct hemisphere, a
corresponding mirror region (at the same distance from the MSP and of the
same shape) in the non-infarct hemisphere is examined. The size of region
is calculated.

[0043]The set of steps indicated as 5 in FIG. 1 is then performed for each
of the segmented hyperintense regions of the infarct hemisphere.

[0044]First, in step 6, it is determined if the size of segmented region
of the infract hemisphere is less than 5% of the total image size
(excluding the background).

[0045]If the result of the determination of step 6 is "no", then the
situation is as in FIG. 2(b). The method then initiates (step 7) a
process of comparing the two symmetrically related regions. This is done
by carrying out the set of steps 8 to 11 once for each pixel of the
region. In each set of steps, we refer to the two symmetric pixels as j
(say in the infarct hemisphere) and j' (say in the non-infarct
hemisphere), and their intensities are denoted pj and pj'
respectively. The error on both the pixels (by assuming that the
intensities of each pixel obey a Poisson distribution) is therefore
{square root over (pj)} and {square root over (pj')}
respectively.

[0046]Let Dj=pj-pj' be the difference of intensities of
pixels j and j'. From the Law of propagation of errors, [18], the total
error on the difference of intensity is obtained (step 8) as:

are partial derivatives and δpj(= {square root over
(pj)}), δpj', (= {square root over (pj')}) are
errors on intensity of pixels j and j'.

[0047]We use this error to estimate the 95% confidence interval around the
difference of intensities equal to 0. The pixels (j and j') are
considered to have similar intensities (i.e. there is no evidence that
the intensity of one is due to an infarct), if it is determined (step 10)
that the difference of their intensities lies in the 95% confidence
region around zero i.e. Dj≦1.96Tj
(http://mathworld.wolfram.com/ConfidenceInterval.html, accessed Oct. 23,
2007). More generally, the similarity criterion for regarding two pixels
as having similar intensities can be written as
Dj≦λ1Tj, where λ1 is a similarity
parameter. Below we investigate the effects of varying λ1,
which is equivalent to exploring other confidence intervals.

[0048]Alternatively, if the result of the determination of step 6 is
"yes", then the situation is as in FIG. 2(a). In this case, the process
initiates a comparison of the two symmetrically-related regions (step
12). Assume there are n pixels in any arbitrary region k and the mean
intensity Rk of the region is:

R k = 1 n j = 1 n p j ##EQU00003##

[0049]The error Ek in Rk is derived from the Law of propagation
of errors [18] as:

[0052]Here, δRk and δRk, are errors on Rk and
Rk' which are defined as Ek and Ek'. Any two regions k and
k' are considered to have similar intensities if it is determined (step
14) that Dk≦1.96Tk. Similar regions are symmetric
regions with similar intensities. More generally, the similarity
criterion can be varied, such that it is expressed as
Dk≦λ1Tk where λ1 is again the
similarity parameter, to explore other confidence intervals.

[0053]The symmetric regions and symmetric pixels with similar intensities
are considered as artifacts. Specifically, if the determination in steps
9 and 14 is negative, the pixel in the infarct hemisphere is excluded
from the set of identified infarct pixels (steps 10 and 15 respectively).
Otherwise, it is confirmed that the pixel is indeed an infarct pixel.

[0054]The flowchart diagram of different steps of determining 3-D spatial
coherence is given in FIG. 4. The input to the method is a 3D MRI image
(typically, a plurality of 2D MRI scans in parallel, spaced-apart planes)
21.

[0055]In step 22 we perform the following set of image processing
sub-steps.

[0056]First, image dilation [19-20] is performed using a structuring
element obtained by taking into account the spatial error around each
pixel [http://www.cis.rit.edu/htbooks/mri/,
http://www.sunnybrook.ca/research/groups/cardiac_mri/MR_background]. The
surrounding region around each pixel can be regarded as the spatial error
region.

[0057]The structuring element is illustrated in FIG. 5. The ith pixel
is the central pixel of the diagram, with co-ordinates (x, y). The
minimum error region around the ith pixel is identified as 1
pixel-wide band surrounding the ith pixel in all directions which is
the 3×3 pixel square ABCD in FIG. 5. We call square ABCD the 1
pixel relationship square. Similarly, square PQRS in FIG. 5 is a 2 pixel
relationship square. In our investigations, the size of the spatial error
square was varied from 3×3 pixels to 11×11 pixels. No
dilation corresponds to maximum artifact removal (but can has a higher
risk of removal of infarct regions) while dilation with 11×11
pixels connects the entire image which makes all the regions spatially
coherent. So, in our experimental results we used a structuring element
for dilation which was a middle value spatial error square of 7×7
pixels (i.e. the i-th pixel is surrounded by 3 pixels in each direction,
which is a structuring element larger than the one shown in FIG. 5).

[0058]Second, we determine 3-D connected regions in the volume [21]. Note
that in each region, the dilated regions have a slightly different shape.
The criterion for deciding that regions in consecutive scans are
connected is that at least one pixel must be in common between the
hyperintense regions of the consecutive scans.

[0059]Third, we calculate the number of slices in which a 3-D connected
region occurs continuously, which is called slice frequency ν. For
example in FIG. 6, which shows a series of consecutive 2D scans, region 1
has ν=5 as it occurs in 5 consecutive slices. Region 2 has ν=2 and
regions 3, 4, 5 and 6 have ν=1.

[0060]Fourth, we determine the maximum ν which is denoted ν.

[0061]In step 23 we determine whether νmax/total infarct slices is
a greater than a parameter indicating a significant fraction of the total
number of slices. For example, for cases in which the number of
interfarct slices is greater than one, we may take the significant
fraction as 0.9. If this determination is negative, the process stops
(step 24).

[0062]Otherwise, in step 25 find any regions with ν equal to 1. Note
that a region may have ν equal to 1 even if a similar region appears
at the same location (in the 2D space of the scans) after a gap of one of
more slices (e.g. regions 2, 4 and 6 in FIG. 6). So for each region with
ν equal to 1 a search is made to find corresponding regions in other
slices. The regions with no counterpart are regarded as isolated regions
which are identified as artifacts (step 26) and eliminated from the set
of identified infarct regions. Conversely, regions for which ν equal
to 1 but there are counterpart regions (irrespective of how far apart the
two scans are which have counterpart regions) and also regions for which
ν is greater than 1, are confirmed as being infarct regions (step 27).

2.1 First Application: False Positive Slice Reduction

[0063]The first application of the processes above (especially the first
process) is for identification of slices for which in fact there is
insufficient evidence that infarct material is present. A flowchart to
show the application is displayed in FIG. 7 and the details are presented
below. Note that there is some overlap between the flow diagram of FIG. 7
and that of FIG. 1 as explained below.

[0064]The input to the application is a set of slices which have been
identified as likely to contain infarct material, for example by an
existing automatic slice identification algorithm [14] which also obtains
the hemisphere in which the infarct is likely to be. This existing
algorithm can be regarded as a first step 31 of the application, and
corresponds to part of step 2 of FIG. 1.

[0065]In step 32 the hyperintense regions in infarcted hemisphere are
obtained by excluding the pixels below a threshold value, say ψ. The
value of ψ is obtained as follows. As mentioned above, the second
peak in the intensity distribution of a DWI scan (e.g. FIG. 1) represents
the normal tissue region (or isointense region). If we approximate the
normal tissue intensity distribution to Gaussian distribution [1], the
intensity at the peak maximum (T1) represents the approximate boundary of
the hyperintense and isointense normal tissue region. We then ignore that
pixels with a threshold less than (T1), and determine mean (RH), and
the total error on the mean (EH), of intensity of the remaining
non-infarct hemisphere pixels.

[0066]We now set ψ=RH+λ2EH where λ2 is
a second similarity parameter, and exclude all pixels with a lower
intensity (step 34). Steps 33 and 34 correspond to step 3 of FIG. 1. For
the experimental results presented below, we used λ2=1.96
(corresponding to 95% confidence interval about the difference of zero).
However, below we also explore other confidence intervals by varying
λ2 to explore the effect on results.

[0068]In step 38, we determine the number of infract pixels remaining in
the slice after the exclusion, and whether this number of residual pixels
is above or below a tolerance parameter. If the number is below the
tolerance parameter, the slice is a false positive slice. If the number
is above the tolerance parameter, the slice is confirmed as being an
infarct slice.

[0069]In our experiments, we have taken the tolerance parameter as 0.01%
of the total number of pixels in the image after excluding the
background.

[0070]FIG. 8 shows the results of applying the first application to a
false positive slice. The infarct hemisphere is represented by I and the
non-infarct hemisphere by N. FIG. 8(a) shows the false positive slice
which is input to the method and after the identification of the MSP.
FIG. 8(b) shows the infarct hemisphere. FIG. 8(c) shows the infarct
hemisphere after removal of isointense regions (i.e. after step 34). FIG.
8(d) shows the image after the corresponding regions in non-infarct
hemisphere N have been added. FIG. 8(e) shows the image after removal of
regions with Dk≦1.96Tk. It will be seen that this is
almost totally dark, so that the number of bright regions is not equal to
the tolerance parameter, and the scan is identified as a false positive.

[0071]FIG. 9 illustrates corresponding results from a slice containing
infarct material. Again, the infarct hemisphere is represented by I and
the non-infarct hemisphere by N. FIG. 9(a) shows the input infarct slice.
FIG. 9(b) shows the infarct hemisphere. FIG. 9(c) shows the infarct
hemisphere after removal of the isointense region. FIG. 9(d) shows the
image after the re-introduction of the corresponding regions in
non-infarct hemisphere. FIG. 9(e) shows the image after removal of
similar intensity regions. It will be seen that there are several bright
regions, in fact a number of bright pixels above the tolerance parameter,
and the scan is confirmed as a true infarct scan.

2.2. Second Application: Artifact Reduction in Infarct Slices

[0072]The second application is illustrated in FIG. 10. This application
employs the first process (FIG. 1) and second process (FIG. 7), so there
is some overlap between FIGS. 1, 7 and 10.

[0073]A first step 41 of the algorithm of FIG. 10 is sub-steps to identify
the hyperintense regions, which are then taken as candidate infract
regions. Step 41 can be carried out by a known algorithm for automatic
infarct segmentation from DWI volume data [15].

[0074]The next step 42 of the algorithm is to obtain the hemisphere which
contains the infarct (this can be done, for example, by the method
disclosed in [14]), and exclude all the hyperintense segmented regions in
the other ("non-infarct") hemisphere. That is, any regions of the
non-infarct hemisphere which had previously been considered be candidate
infarct regions, are relabeled such that they no longer are. This
corresponds broadly to steps 2-3 of FIG. 1.

[0076]The algorithm next (step 45) identifies further artifacts based on
3-D spatial coherence (i.e. the process of FIG. 4), and (step 46) removes
those artifacts. This is the second process which is described in FIG. 7.

[0077]The steps of artifact removal are displayed in FIG. 11. FIG. 11(a)
shows the original slice. FIG. 11(b) shows the segmented slice. FIG.
11(c) shows the image after the artifacts in the non-infarct hemisphere
are removed. FIG. 11(d) shows the result of symmetric artifact removal.
FIG. 11(e) shows the result of removing the spatially incoherent regions.

3.1. Materials

[0078]We now present experimental results using the processes and
applications described above. Fifty one DWI scans were used in this
study. This is data the we had used before.

(i) To test automatic slice identification (i.e. the application of FIG. 7
we used 36 data set used by [14]. The DWI scans had in-plane resolutions
of 0.9 mm×0.9 mm to 2.4 mm×2.4 mm, slice thickness of 4-14
mm, and number of slices from 4 to 36.(ii) To test automatic infarct
segmentation (i.e. the application of FIG. 10 we used 13 DWI cases used
by [15]. The DWI scans had in-plane resolutions of 1 mm×1 mm or 1.5
mm×1.5 mm, and 5 mm slice thickness. The number of slices in DWI
scans was from 27 to 33. The matrix size of DWI scans was 256×256.
Note that the 13 DWI cases are a subset of 36 dataset used in [14].(iii)
15 additional data set were used to demonstrate application of the
proposed algorithm to improve results of a third algorithm [16]. The DWI
scans had in-plane resolutions of 1.17 mm×1.17 mm to 2.42
mm×2.42 mm, slice thickness of 6.5-7 mm, and number of slices from
15-20.

[0081]Out of 36 cases, 26 cases showed an improvement in results due to
false positive slice removal. The results of remaining 10 cases were
unaffected by the processing. If we consider only those cases in which
the results changed, the change in results is as follows: initial results
for 26 data: (sensitivity, specificity, DSI)=(0.982, 0.474, 0.586). After
processing the results for 26 data: (sensitivity, specificity,
DSI)=(0.958, 0.664, 0.677). Thus, an increase in specificity and DSI is
observed by 19% and 9.1%, respectively, with a decrease in sensitivity by
2.4%. The false negative slices which were removed had an insignificant
fraction of area as compared to the maximum area of infarct in a slice.
By using the proposed algorithm we are able to remove 31% of the false
positive results.

[0082]FIG. 12 displays the overall change in sensitivity, specificity and
DSI as a result of the current algorithm. The left (light grey) bar of
the histogram indicates the results obtained in [14], while the
corresponding right (darker) bars shown the results of the algorithm of
FIG. 7.

[0083]FIG. 13(a) shows the effect of changing λ1 and
λ2(which, as described above, are employed in the criteria
Dk≦λ1Tk and RH+λ2EH,
respectively) on the sensitivity of infarct slice identification, and
FIG. 13(b) shows the effect on the specificity of infarct slice
identification. The vertical axes show sensitivity and specificity
respectively. The sensitivity remains more or less unchanged for values
of λ1 and λ2 less than 2. For λ1 and
λ2 greater than 2, sensitivity starts decreasing steeply
(since even the infarct region starts getting eliminated) and attains the
value of 86.7% at λ1 and λ2 equal to 3. The
specificity increases continuously with increase in value of
λ1 and λ2 to 3 (=79.3%). Since high sensitivity is
important, we have used values of both the parameters as 1.96 where the
(sensitivity, specificity) are (96.6%, 66.6%).

[0084]The overall increase in (specificity, DSI) is (15.2%, 6.9%) with
decrease in the sensitivity by only 1.5%.

3.3 Artifact Reduction

[0085]The results of infarct segmentation algorithm in [15] were as
follows: sensitivity=0.81, specificity=0.99 and DSI=0.60. After
processing the data with proposed algorithm (i.e. the application of FIG.
10), the results are: sensitivity=0.793, specificity=0.993, and
DSI=0.676.

[0086]Out of 13 volumes, all the cases showed an improvement in results
due to artifact removal. Only 6 cases had DSI<0.5 out of the total of
13 cases. The average DSI for these 6 cases before processing with
current algorithm was 18.3%. After processing with the proposed
algorithm, the average increase of DSI is 11.2%. The seven cases which
had an average DSI of 74.7% after post processing increased by 4.7%. Thus
effect of current processing is more significant in cases where there are
a large number of artifacts. The fraction of false positive pixels
removed by the current algorithm is 71%.

[0087]FIG. 14 displays the overall change in sensitivity, specificity and
DSI as a result of the current algorithm. The left (light grey) bar of
the histogram indicates the results obtained in [15], while the
corresponding right (darker) bars shown the results of the algorithm of
FIG. 10.

[0088]Since the number of true negative pixels is of the order of total
slice pixels and is much larger than the false positive pixels,
specificity is always very large. It is hardly affected by any change in
the number of false positive pixels. That is why no change in specificity
is observed in FIG. 14. Therefore, DSI is a better measure to study in
this case as it is independent of true negative pixels. The overall
improvement in DSI is 7.6%.

[0089]In [16] we segmented 15 data (5 each of low, medium and high
artifact density). In another test of the application of FIG. 10, the
results from [16] were then processed using the application of FIG. 10.
The average change in (sensitivity, specificity, DSI) was from (74.02,
99.69, 67.32) % to (72.27, 99.87, 72.4) %. The DSI showed an improvement
of 5.1%.

4.1 Discussion

[0090]One of the goals of stroke CAD is to accurately and automatically
identify, segment and measure the stroke region. This is important (a) in
context of thrombolysis which requires quantifying the diffusion
perfusion mismatch and size of infarct to that of MCA ratio (b) to
provide input parameters for studies involving prognostic information
like quantifying the impact of infarct location on stroke severity [23],
quantification of patterns of DWI lesions [24] etc. While the
state-of-the-art algorithms are being developed for achieving the final
goal of stroke CAD, the presently proposed algorithms have stand alone
applications in related areas of research. The embodiments make it
possible to discriminate infarcts and artifacts based on the following
two observational properties in DWI scans. They are motivated by two
observations:

(i) A first observation is that a normal DWI scan in an axial plane shows
both the hemispheres to have approximately similar features in terms of
intensity, shape etc [e.g. 25]. Thus, if a DWI scan shows symmetric
hyperintense regions in both hemispheres, they are most probably
artifacts. An infarct caused by vessel occlusion most likely occurs in a
single hemisphere so it will be much more hyperintense than the symmetric
region in the opposite hemisphere. The embodiments quantify significant
difference of intensity using the Poisson distribution in the intensity
space of each pixel.(ii) The second observation is that the infarct
regions in different slices show spatial coherence. The regions that
occur at distant locations from the spatially coherent regions are most
probably artifacts. The embodiments detect spatial coherence by
determining the overlap of dilated regions in different slices.

[0091]In many cases even the artifacts show 3-D spatial coherence. For
that reason, the embodiment of FIG. 10 processes 3-D spatial coherence
after symmetric artifact removal, which reduces the chances of artifacts
exhibiting 3-D spatial coherence. From our observations [15], it is very
rare to find an artifact which is symmetric to an infarct. So the chances
of an infarct being removed by an algorithm which employs 2-D symmetry
(such as that of FIG. 1) is very low, which in turn enhances the chance
of the result of the algorithm being spatially symmetric.

[0092]This is illustrated in FIG. 15. The column of 3 slices shown in FIG.
15(a) includes artifacts (in the boxes labeled A1, A2 and A3) which are
symmetric and spatially coherent. By the process of FIG. 1, the symmetric
artifacts removed in slices 1-3 to give the scans of FIG. 15(b), in which
only the artifact in box B3 has survived. The artifact in box B3 is
removed by evaluating the 3-D spatial coherence of that region by a
process of FIG. 4, to give the result shown as the three scans of FIG.
15(c).

[0093]The embodiments also make use of the observation that the error in
the intensity of each pixel is given by a normal distribution which is
independent of the nature of the experiment and is generally associated
with the randomness of the outcome of the experiment. However, since the
mean and standard deviation of the normal distribution are independent,
this distribution cannot be used to predict errors in the cases where it
is not possible to repeat the outcome of experiment [1]. In fact, the
present inventors initially considered using the FWHM of the background
peak (as shown in FIG. 2) as an estimate of error on every pixel. Example
results from doing so are presented in FIG. 16. However, when we compared
the differences of pixel intensities, even at the level of 3 sigma
confidence interval around the difference of zero, there were residuals
at hyperintense pixel regions (note that, by contrast, in the FIG. 1
process, we have used 1.96 sigma confidence interval around the
difference of zero). This implies that larger errors need to be assumed
for brighter pixels. This increases our confidence in an assumption of
Poisson errors since, by definition, errors are proportional to the
intensity.

[0094]Identifying pixels which are symmetric about the MSP and have
similar intensities to remove symmetric artifacts is very sensitive to
errors such as: inherent asymmetry in hemispheres, the inter-hemispheric
fissure being curved to a greater extent, etc. For that reason, the
present embodiments by preference consider symmetric regions instead of
individual pixels for the purpose of comparing the intensities. Even
while considering the symmetric regions, due to inherent asymmetry of
hemispheres about the MSP, regions lying close to the cortical surface
boundary or too close to the ventricles or cerebrospinal fluid (CSF) may
contain part of background or parenchyma. This is shown in FIG. 17, where
the ventricle V which lies on the MSP has obscured a part of the bright
region adjacent to it on the N hemisphere, and further asymmetry is
caused by the part of the bright region at the upper right of the N
hemisphere which is close to the cortical surface. However, since the
embodiments exclude pixels with intensity below T1, the mean of region
intensity is not affected by the hypointense pixels due to the background
or the parenchyma.

[0095]Based on the quantification of small, middle and large artifacts in
[14], large infarct regions are expected to be those which are above
about 5% of the image (excluding the background). Such large regions
might have greater errors due to inhomogeneity (e.g. caused by intra
slice variation during data observation). For this reason if a region is
considered as a large region, the embodiment of FIG. 1 performs pixel to
pixel comparison. Pixel to pixel comparison may disintegrate large areas
but does not completely remove them.

[0096]The 3-D spatial coherence is tested only in the cases where the
ratio of νmax to the total number of infarct slices is at least
equal to a ratio which is considered significant. The choice of high
significant ratio as 0.9 is considered to establish the confidence that
the infarct occurs at the similar location in the slices (cases with
number of infarct slices=1 are excluded for this analysis). This is done
to avoid cases in which the infarct occurs at multiple locations and
there may be a significant chance that a spatially isolated region is an
infarct. In the case that an infarct occurs mainly in one region, the
chances that spatially isolated regions represent artifacts are high.

[0097]One of the assumptions for this algorithm is that the artifacts are
symmetric about the MSP. Infarcts that are caused by vessel occlusion are
most likely to be located in one hemisphere. In some rare cases (e.g.,
caused by a sudden drop in blood pressure in the presence of bilateral
stenosis), infarcts may be present in both the hemispheres almost
symmetrically. In such cases, only the spatial coherence test of the
regions is performed. Symmetry based artifact removal should not be
performed then. During data acquisition, the head may be tilted such that
there is a significant roll angle. In such cases, the symmetry about the
midsagittal plane is violated and 2-D symmetry based comparison of
hyperintense regions may be erroneous.

[0098]When an artifact is symmetric to an infarct region, there may be a
chance of losing an infarct region. In addition, there are various
reasons because of which artifacts may be retained by the present
embodiments, including: [0099](i) Inhomogeneity within the slice and
the volume. If so, applying intra-slice and inter slice inhomogeneity
corrections [e.g. 26-27] can further help to enhance the results.
[0100](ii) Asymmetric artifacts close to an infarct region may not get
identified by any of the criteria studied in the embodiments. [0101](iii)
Large artifacts may be only fragmented rather than completely removed as
we apply pixel wise comparison of such regions. Note that, as mentioned
above, pixel-wise comparison generally does not completely remove the
whole region.

[0102]Nevertheless, it is clear from the experimental results that the
embodiments provide a fast and pragmatic approach for artifact removal.
They do not utilize anatomy related information whose processing may be
challenging and more time consuming. The present approach also does not
take into account the location of infarct, which is critical [23-24], and
influences the outcome.

[0103]The embodiments can be applied as a stand alone post processor or be
a part of a stroke CAD system, and can provide a fast post-processing
tool to reduce artifacts in infarct processing applications. In fact, the
processing time for the present embodiments, when implemented on a matlab
platform, was under 1 second.